Improved Tree Search for Automatic Program Synthesis

However, as reported by previous work (Zohar & Wolf, 2018; Chen et al., 2019), employing a reinforcement learning In the task of automatic program synthesis, one approach, as opposed to training using a maximum likelihood obtains pairs of matching inputs and outputs and loss to generate the single program that is available generates a computer program, in a particular as the ground truth, either hurts performance or leads to a domain-specific language (DSL), which given small increase in performance. This is despite training the each sample input returns the matching output. A MLE approach in a teacher-forcing way, in which, during key element is being able to perform an efficient training and unlike during test time, the partial programs search in the space of valid programs. Here, we considered are the prefix of the ground truth programs.